{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c1a20a47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello word!\n"
     ]
    }
   ],
   "source": [
    "print('hello word!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "87515db0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "time.sleep(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "054e1c8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def square(x):\n",
    "    return x * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5324001f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 squared is 16\n"
     ]
    }
   ],
   "source": [
    "x = np.random.randint(1, 10)\n",
    "y = square(x)\n",
    "print('%d squared is %d' % (x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "252138bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def selection_sort(array):\n",
    "    for i in range(len(array)-1):\n",
    "        min_index = i\n",
    "        for j in range(i+1, len(array)):\n",
    "            if array[j] < array[min_index]:\n",
    "                min_index = j\n",
    "        if min_index != i:\n",
    "            array[i], array[min_index] = array[min_index], array[i]\n",
    "    return array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a47eea39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5, 7, 10, 15, 17, 21, 24, 27, 30, 36, 45, 50]\n"
     ]
    }
   ],
   "source": [
    "array = [10, 17, 50, 7, 30, 24, 27, 45, 15, 5, 36, 21]\n",
    "print(selection_sort(array))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "23f8836f",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fb28f47c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('fortune500.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fa1778f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Company</th>\n",
       "      <th>Revenue (in millions)</th>\n",
       "      <th>Profit (in millions)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1955</td>\n",
       "      <td>1</td>\n",
       "      <td>General Motors</td>\n",
       "      <td>9823.5</td>\n",
       "      <td>806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1955</td>\n",
       "      <td>2</td>\n",
       "      <td>Exxon Mobil</td>\n",
       "      <td>5661.4</td>\n",
       "      <td>584.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1955</td>\n",
       "      <td>3</td>\n",
       "      <td>U.S. Steel</td>\n",
       "      <td>3250.4</td>\n",
       "      <td>195.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1955</td>\n",
       "      <td>4</td>\n",
       "      <td>General Electric</td>\n",
       "      <td>2959.1</td>\n",
       "      <td>212.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1955</td>\n",
       "      <td>5</td>\n",
       "      <td>Esmark</td>\n",
       "      <td>2510.8</td>\n",
       "      <td>19.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Rank           Company  Revenue (in millions) Profit (in millions)\n",
       "0  1955     1    General Motors                 9823.5                  806\n",
       "1  1955     2       Exxon Mobil                 5661.4                584.8\n",
       "2  1955     3        U.S. Steel                 3250.4                195.4\n",
       "3  1955     4  General Electric                 2959.1                212.6\n",
       "4  1955     5            Esmark                 2510.8                 19.1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e7a51cdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Company</th>\n",
       "      <th>Revenue (in millions)</th>\n",
       "      <th>Profit (in millions)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25495</th>\n",
       "      <td>2005</td>\n",
       "      <td>496</td>\n",
       "      <td>Wm. Wrigley Jr.</td>\n",
       "      <td>3648.6</td>\n",
       "      <td>493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25496</th>\n",
       "      <td>2005</td>\n",
       "      <td>497</td>\n",
       "      <td>Peabody Energy</td>\n",
       "      <td>3631.6</td>\n",
       "      <td>175.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25497</th>\n",
       "      <td>2005</td>\n",
       "      <td>498</td>\n",
       "      <td>Wendy's International</td>\n",
       "      <td>3630.4</td>\n",
       "      <td>57.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25498</th>\n",
       "      <td>2005</td>\n",
       "      <td>499</td>\n",
       "      <td>Kindred Healthcare</td>\n",
       "      <td>3616.6</td>\n",
       "      <td>70.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25499</th>\n",
       "      <td>2005</td>\n",
       "      <td>500</td>\n",
       "      <td>Cincinnati Financial</td>\n",
       "      <td>3614.0</td>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Year  Rank                Company  Revenue (in millions)  \\\n",
       "25495  2005   496        Wm. Wrigley Jr.                 3648.6   \n",
       "25496  2005   497         Peabody Energy                 3631.6   \n",
       "25497  2005   498  Wendy's International                 3630.4   \n",
       "25498  2005   499     Kindred Healthcare                 3616.6   \n",
       "25499  2005   500   Cincinnati Financial                 3614.0   \n",
       "\n",
       "      Profit (in millions)  \n",
       "25495                  493  \n",
       "25496                175.4  \n",
       "25497                 57.8  \n",
       "25498                 70.6  \n",
       "25499                  584  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "aee8e8af",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['year', 'rank', 'company', 'revenue', 'profit']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "80632371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25500"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4e64ca47",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year         int64\n",
       "rank         int64\n",
       "company     object\n",
       "revenue    float64\n",
       "profit      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "df99227b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>rank</th>\n",
       "      <th>company</th>\n",
       "      <th>revenue</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>1955</td>\n",
       "      <td>229</td>\n",
       "      <td>Norton</td>\n",
       "      <td>135.0</td>\n",
       "      <td>N.A.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>1955</td>\n",
       "      <td>291</td>\n",
       "      <td>Schlitz Brewing</td>\n",
       "      <td>100.0</td>\n",
       "      <td>N.A.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>1955</td>\n",
       "      <td>295</td>\n",
       "      <td>Pacific Vegetable Oil</td>\n",
       "      <td>97.9</td>\n",
       "      <td>N.A.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>1955</td>\n",
       "      <td>297</td>\n",
       "      <td>Liebmann Breweries</td>\n",
       "      <td>96.0</td>\n",
       "      <td>N.A.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>1955</td>\n",
       "      <td>353</td>\n",
       "      <td>Minneapolis-Moline</td>\n",
       "      <td>77.4</td>\n",
       "      <td>N.A.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  rank                company  revenue profit\n",
       "228  1955   229                 Norton    135.0   N.A.\n",
       "290  1955   291        Schlitz Brewing    100.0   N.A.\n",
       "294  1955   295  Pacific Vegetable Oil     97.9   N.A.\n",
       "296  1955   297     Liebmann Breweries     96.0   N.A.\n",
       "352  1955   353     Minneapolis-Moline     77.4   N.A."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_numberic_profits = df.profit.str.contains('[^0-9.-]')\n",
    "df.loc[non_numberic_profits].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "25bcbbf3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "369"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.profit[non_numberic_profits])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a38529ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_sizes, _, _ = plt.hist(df.year[non_numberic_profits], bins=range(1955, 2006))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7c3af7fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.loc[~non_numberic_profits]\n",
    "df.profit = df.profit.apply(pd.to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "211e8ace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25131"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "69557e5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year         int64\n",
       "rank         int64\n",
       "company     object\n",
       "revenue    float64\n",
       "profit     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "34454459",
   "metadata": {},
   "outputs": [],
   "source": [
    "group_by_year = df.loc[:, ['year', 'revenue', 'profit']].groupby('year')\n",
    "avgs = group_by_year.mean()\n",
    "x = avgs.index\n",
    "y1 = avgs.profit\n",
    "def plot(x, y, ax, title, y_label):\n",
    "    ax.set_title(title)\n",
    "    ax.set_ylabel(y_label)\n",
    "    ax.plot(x, y)\n",
    "    ax.margins(x=0, y=0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dd50f7bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "plot(x, y1, ax, 'Increase in mean Fortune 500 company profits from 1955 to 2005', 'Profit (millions)')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4935cbc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y2 = avgs.revenue\n",
    "fig, ax = plt.subplots()\n",
    "plot(x, y2, ax, 'Increase in mean Fortune 500 company revenues from 1955 to 2005', 'Revenue (millions)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "37a9f0ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rHTcHacwJx5C7f9rdnxGv04mqNTeqdn3N+P80nhH3z7v7+e6+DviTOB8/nOrtTP+/nG7+5H046+02syuA/we8LL7hUPUwkIo7Dayq/a7M9D2bzXYsdTpHT03naJ2jq/nRObq+f2d25+ja/+1M/5evAevN7AlEgf6n4/ReouD28TXLrXL3RoPd6Y6lfcC3JuWpy93fAtP+hjWqdvsb+V40hbvf4+7Pdvd17v4iotp5d0719vh5unN5o+d5mOV2WzRKxTeIanlNvoE03fftPuB0M+ueYn69vM543CvAj7n7IaK7Lh8xszVmljazZ02zyD8A18f/UMxsg5ldGc/rJqq2dJzoy/kX1YXMLGNmrzOzVfHdqSGiUgWILgB/w8yeYpFOM3vppH96K/wDcJ2N9+y8ysyqw2Z1Ex3wx4guVP8/TrwT1aiTWdf34mXfZmYpM3slUVvHuszs1Wa2PX7ZT/SFqf4fjhD9iFTdDLzJzM4zsw6iYGIuPgO8w8xOi09cf0HUcVDdXsqJ9kM4KS8/AZ5lZqeY2SqiqnON+hTwMjN7UVyC1WZmz6nZD2PMbHX8vrZ4f76OqIrlV+O3fBE438x+yczagP8PuMejklWIqlf+rplts6iE+J1E1e/q+Vfg+RYNi5ayaCz3J7h7QLTvrzez7vi79rvxdszVS8zsGWaWIero6Afuvo+TPI7jksPPE53Y73T3vTMs8lYz225ma4E/5MSe0Wt9Bvjj+DdmPdG+bnQfTDiWzewcM3uemWWJ2snlGT/uZ6VJ/58x1WOU6I55Ij4W0zXzL47fs4GoauKXq8efmb3YzDbF0+cSVeH/Uvx6pmN7siPA9viYmfV2m9nziI7xX3L3CRcm7j5K1K7vT+Pf+6cTBZ7Vi4Npv2dmdmV8vjIzuxR4W3U7lxudo6elc7TO0TpHT2MO5+ha0/5f4uPj88D/Jboh8fU4PST6zfhbM9sIEO/vFzX4uT8BXmlmHRbd3LqmZt5/AGeb2Rvi38K0mT3ZomHlpvsNm4vZfi8aFv+OthHVBCHet9ma+RfGaR1m9ntEtSY+Hs97SnxNkzCzdcDfETUPGYznz+b8WO+73PB2m9k24Dbg7939H+qsf8rvm7s/TPS//pN4W3+RqJr/F+J1Pyf+PTGLhjV87zTbMUYB/kRvILob+SBRO5jfmea9HyTqOONrZjZM1JnPU+J5nyCqynGAqNro9+t8zm6Lqgb+BnG1IXe/i6j95YeJTmi7iNrctJS7f5GolO+mOM8/A14cz/4q0UXXw0TbXGDuVdjmvC53LwGvJNpf/UTtk/5tmkWeDPzAzEaI/o9vj6u+QFR9+UaLqj29xt1vJeoo5Tai/8lts9qqcf9MdOH+baKOMgpEnYVNtU05onY4343zcpm7f50oGLyHqBfZ/2j0w+OT5JVEAeUxon37+9T/HUgTdYhT7cDnt4FXuPtD8bqOEVUBu55ofz+FqDfZqn8k6kzmXqLj5T/jtHr52ktUleydRNXxfsL4nc7fJrqL/ShR79qfJtqPc/Vpoou/PuBioiptMD/H8Y1EbQxnqvpXzcfXiLbrUaJ9PZU/Jxri6B6i/fmjGd5f62PAefHx8+9EbTXfS/Q/PUx0d/8PG1xXPfP9/6n1BqIbEB8laqueJ7pYqvogUfvZh+LnX6uZdzlwj5mNAl8h+i2oBnHTHtt13EZ0J/2wmVWr1s9mu/83USnxV2x8DNtba+b/JlFHRkeJLije4u73QUPfs6uIfpOGic477/NGhs9ZunSOrkPnaJ2j0Tm6EbM5R49p8P/yaaIaQZ+bFAC+i+iY/H783fwGUVvuRvwt452r3UhNrQN3HyZqk34VUUnzYcY7GYUpfsPmaFbfi1k6lejcXi2tzhOd06veQFRD5yjRef0F7l6M551O1ORhmOgYLjLeVAJmcX6s911mdtv9v+L8/EnNeX6kZv5M37eriPoT6Ce6RntV/B2GqAPF7xF9z+6Il3/bFPkYU+2ZVkRkWTOzjxN1JvPHTVr/KUSBx2Z3H5rmfbuJOs75RjPyISIistQslnO0yHKgEnwRkZNkZgmiqok36cJBRERk8dA5Wlaaur2kiohIYyzqvOgIUbXBK1qcHREREYnpHC0rkaroi4iIiIiIiCwDqqIvIiIiIiIisgys2Cr669ev9507d7Y6GyIissLM12D1d999d6+7b5iHVS1JOo+LiMhS5cCPmnQeX7EB/s6dO7nrrrtanQ0REVlhekeKrO/KzvzGGZjZnnnIzpKl87iIiCxVx4aLbOxpa8p5XFX0RUREFtBAroT6vxEREVmZhgtlipWgaetXgC8iIrJAgtDJl0KKlbDVWREREZEWOD5Saur6FeCLiIgskHw5umNfLCvAFxERWWmKlYDhQqWpn6EAX0REZIHkitFJvZlV80RERGRx6httbuk9KMAXERFZMLlSFNgXVIIvIiKyooSh0z9abvrnKMAXERFZINUAXyX4IiIiK8tAvkwQNr+TXQX4IiIiC6BYCcZO7MVKqJ70RUREVpC+0eKCfI4CfBERkQWQK46X2rujnvRFRERWiFypQr60MOd9BfgiIiILIFeeWC1/KQX4ZpY0sx+b2X/Er9ea2dfN7Ofx85qa915nZrvM7CEze1FN+sVmdm887+/MzOL0rJl9Nk7/gZntXPANFBERaaJmD41XSwG+iIjIAsiXJg6LUywvqXb4bwceqHn9buCb7n4W8M34NWZ2HnAV8HjgCuAjZpaMl/kocC1wVvy4Ik6/Buh39zOBvwXe19xNERERWTiVIGQw3/zO9aoU4IuIiDRZGPoJPecvlRJ8M9sOvBT4p5rkK4Eb4+kbgVfUpN/k7kV3fwzYBVxqZluAHnf/nkedD3xi0jLVdX0euLxaui8iIrLU9Y2WWMhudxTgi4iINFmuHJxwci8snRL8DwB/ANTekdjk7ocA4ueNcfo2YF/N+/bHadvi6cnpE5Zx9wowCKyb1y0QERFpAXenL7dw1fNBAb6IiEjT5SZVz4el0ZO+mf0CcNTd7250kTppPk36dMvUy8+1ZnaXmd117NixBrMkIiLSGkP5CuXKwp7rFeCLiIg0Wb50Ymn9EulJ/+nAy81sN3AT8Dwz+xRwJK52T/x8NH7/fmBHzfLbgYNx+vY66ROWMbMUsAroq5cZd7/B3S9x90s2bNhw8lsnIiLSRMcXaGi8WgrwRUREmmy0WL86/mIP8N39Onff7u47iTrPu83dXw/cAlwdv+1q4Evx9C3AVXHP+KcRdaZ3Z1yNf9jMLovb179x0jLVdb0q/ozFXbVBRERkBoVyMOX5v5lSC/6JIiIiK0ixEhCE9ePVYjmA9vQC52hevBe42cyuAfYCrwZw9/vM7GbgfqACvNXdq1c3bwE+DrQDt8YPgI8BnzSzXUQl91ct1EaIiIg0y/HRhW17X6UAX0REpInqVc+vWuwl+LXc/Xbg9nj6OHD5FO+7Hri+TvpdwPl10gvENwhERESWgyB0+lsU4KuKvoiISBPlpgnwl1BP+iIiItKg/tzCDo1XSwG+iIhIE9XrQb9qKfSkLyIiIrPT16LSe1CALyIi0jRh6BTKU1fDXyI96YuIiEiDjo8UKU5z7m82BfgiIiJNki8HM1bRU4AvIiKyPORKFQ4NFlqaBwX4IiIiTTJd+/uqotrhi4iILHmVIGTP8VzL2t5XKcAXERFpkuna31epBF9ERGRpc3f29uWoBK3vV0cBvoiISJM0UoKvnvRFRESWtsNDBUaLi+N8rgBfRESkCUqVsKE7+epJX0REZOkazJXpHW5dr/mTKcAXERFpgnwDpfcQ9aRfClRNX0REZKkplAP2D+RanY0JWh7gm1nSzH5sZv8Rv15rZl83s5/Hz2tq3nudme0ys4fM7EU16Reb2b3xvL8zM2vFtoiIiFSNNtD+vmq6ofRERERk8QnDqN19uMhO4S0P8IG3Aw/UvH438E13Pwv4ZvwaMzsPuAp4PHAF8BEzS8bLfBS4FjgrflyxMFkXERGpr5H291XFyuJotyciIiKN2d+fb+l491NJtfLDzWw78FLgeuB34+QrgefE0zcCtwPvitNvcvci8JiZ7QIuNbPdQI+7fy9e5yeAVwC3LshGiIiITOLus+o8bzFeIIiIiCxH7k6xElIKQkqV6FGMn8tBSFs6SVc2RWc2SWcmRSJxYuXwY8NFBvPlFuR+Zi0N8IEPAH8AdNekbXL3QwDufsjMNsbp24Dv17xvf5xWjqcnp5/AzK4lKunnlFNOmYfsi4iInChfDmY1Dq5K8EVERJorV6qwry9POQinPUfnSwH5UsCxYTCD9kw14E/RkU6SLwccGSosXMZnqWUBvpn9AnDU3e82s+c0skidNJ8m/cRE9xuAGwAuueQSdVksIiJNMduhctQGX0REpLmOj5QoVWZ3vnWHXDEgVwyAItWe3hbz4DetLMF/OvByM3sJ0Ab0mNmngCNmtiUuvd8CHI3fvx/YUbP8duBgnL69TrqIiEhLNNqDfpV7VIqfTSVnfrOIiIjMSjkI56VK/WIO7Kta1smeu1/n7tvdfSdR53m3ufvrgVuAq+O3XQ18KZ6+BbjKzLJmdhpRZ3p3xtX5h83ssrj3/DfWLCMiIrLgcuXGe9CvUim+iIhIc/SPlpZEcD4fWt0Gv573Ajeb2TXAXuDVAO5+n5ndDNwPVIC3unu1iOQtwMeBdqLO9dTBnoiItEQ5CClXZn8VEbXDT89/hkRERFYwd+f4aKnV2VgwiyLAd/fbiXrLx92PA5dP8b7riXrcn5x+F3B+83IoIiLSmNws299XqSd9ERGR+TeUr1AJVkjxPS2soi8iIrIczaV6PqgnfRERkWY4PlpsdRYWlAJ8ERGReZSbZQd7VWqDLyIiMr8K5WDWI9ssdQrwRURE5kkY+qx70K+q9qQvIiIi82Mltb2vUoAvIiIyTwby5ZPqpbfRUvz+FXjBIiIiMhtB6CvyfKkAX0REZJ4cHzm5dn6NlOCXKiEHB/Mn9TkiIiLLXX9u5QyNV0sBvoiIyDwYLpRPuh19Iz3p7+/PEaq5voiIyLT6VmDpPSjAFxERmRfHR07+QmKmEvzekeKK6yxIRERktkaKlRU7/KwCfBERkZNUKAcMF+Y2PN7E9Ux9MVIoBxweLJz0Z4iIiCx3J9tkbilTgC8iInKS5quX3ql60nd39vfnV2RbQhERkdkoVcJ5uem+VCnAFxEROQmVIJzXXnqLlRNL8Y8NF+c8/J6IiMhK0je6MjvXq1KALyIichL65rmX3kJ5YiCfLwUcHV65VQ1FREQa5e4rtnO9KgX4IiIic9SMC4naToGiqvm5FV0SISIi0qjBfJkgXNknTQX4IiIiczSYL1OuzO+FRG0b/MNDhZMeek9ERGSl6J2HEW2WOgX4IiIic9SMC4lqQD9arNA7rAsVERGRRuRLgfqrQQG+iIjInIwWK025kHCP2uHv78/P+7pFRESWq+Oj6q8GFOCLiIjMyfEmVgPc25ejVKc3fRERETlROQgZyJVbnY1FQQG+iIjILJUqIUOF5l1IFNXuXkREpGG9I0V1SBtTgC8iIjJLx0d1ISEiIrIYVIKwqbXqlhoF+CIiIrMQhitrjF0zazOzO83sp2Z2n5n9nzh9rZl93cx+Hj+vqVnmOjPbZWYPmdmLatIvNrN743l/Z2YWp2fN7LNx+g/MbOeCb6iIiCxJvSMl3XSvoQBfRERkFvpyJcKVVYO+CDzP3S8CngBcYWaXAe8GvunuZwHfjF9jZucBVwGPB64APmJmyXhdHwWuBc6KH1fE6dcA/e5+JvC3wPsWYLtERGSJC0JX53qTKMAXERGZhZVWDdAjI/HLdPxw4Ergxjj9RuAV8fSVwE3uXnT3x4BdwKVmtgXocffvubsDn5i0THVdnwcur5bui4iITOX4SHGl3XSfkQJ8ERGRBg3myyuyd3szS5rZT4CjwNfd/QfAJnc/BBA/b4zfvg3YV7P4/jhtWzw9OX3CMu5eAQaBdXXyca2Z3WVmdx07dmyetk5ERJaiMHR6V9hN90YowBcREWlAGDrHhgutzkZLuHvg7k8AthOVxp8/zdvrlbz7NOnTLTM5Hze4+yXufsmGDRtmyLWIiCxnx0dLBKEa30+mAF9ERGQG7s6+/hz50sorva/l7gPA7URt54/E1e6Jn4/Gb9sP7KhZbDtwME7fXid9wjJmlgJWAX3N2AYREVn6otJ7tb2vRwG+iIjIDA4M5BnKV1qdjZYwsw1mtjqebgeeDzwI3AJcHb/tauBL8fQtwFVxz/inEXWmd2dcjX/YzC6L29e/cdIy1XW9CrgtbqcvIiJygr5ciUqg00Q9qVZnQEREZDE7PFigf7Tc6my00hbgxrgn/ARws7v/h5l9D7jZzK4B9gKvBnD3+8zsZuB+oAK81d2DeF1vAT4OtAO3xg+AjwGfNLNdRCX3Vy3IlomIyJLjrtL76SjAFxERmcKx4SLHhlf2RYS73wM8sU76ceDyKZa5Hri+TvpdwAnt9929QHyDQEREZDr9uTLlikrvp6Iq+iIiInX0j5Y4PLgyO9UTERFZjNx9xd94n4kCfBERkUmGCmUODORbnQ0RERGpsVKHq50NBfgiIiI1RosV9h7PoS7eREREFpejKr2fkQJ8ERGRWKEcsPv4qIJ7ERGRRWYwV6ZYVun9TBTgi4iIAKVKyGO9o4S6dhAREVl0jo2oX5xGqBd9ERFZsdydoUKFgVyJ4UJFJfciIiKL0GCuTL6kO/CNUIAvIiIrzmixQn+uxGC+rBJ7ERGRRcDdKVZCiuWQYiWIpisBhXKoG/CzoABfRERWhGIlYCBXZiCnHnhFREQWi6PDBfpHdW6eLwrwRURk2TsyVODokHreNbM1wFYgD+x2d11NiYhIS4Shc2Agz0Cu3OqsLCst62TPzHaY2X+b2QNmdp+ZvT1OX2tmXzezn8fPa2qWuc7MdpnZQ2b2opr0i83s3nje35mZtWKbRERk8Tk0mF/Rwb2ZrTKzPzSze4HvA/8I3AzsMbPPmdlzW5tDERFZacpByKO9owrum6CVvehXgHe6++OAy4C3mtl5wLuBb7r7WcA349fE864CHg9cAXzEzJLxuj4KXAucFT+uWMgNERGRxengQJ7e4VKrs9Fqnwf2Ac9093Pc/Rnufom77wDeC1xpZte0NosiIrJS5EsBu46OkC8Frc7KstSyKvrufgg4FE8Pm9kDwDbgSuA58dtuBG4H3hWn3+TuReAxM9sFXGpmu4Eed/8egJl9AngFcOtCbYuIiCw++/tz9I+qZMDdXzDNvLuBuxcwOyIisoIN5srs68+p07wmamUJ/hgz2wk8EfgBsCkO/qs3ATbGb9tGVAJRtT9O2xZPT06v9znXmtldZnbXsWPH5nUbRERk8djXp+B+MjN7upl1xtOvN7P3m9mprc6XiIisDEeGCuztU3DfbC0P8M2sC/gC8DvuPjTdW+uk+TTpJya63xBXS7xkw4YNs8+siIgsau7Ovr6c2vTV91EgZ2YXAX8A7AE+0dosiYjIcheGzp7joyu6P5yF1NIA38zSRMH9v7r7v8XJR8xsSzx/C3A0Tt8P7KhZfDtwME7fXiddRERWkCi4V2+806i4uxM1efugu38Q6G5xnkREZBkrVgIeOTbCUL7S6qysGK3sRd+AjwEPuPv7a2bdAlwdT18NfKkm/Sozy5rZaUSd6d0ZV+MfNrPL4nW+sWYZERFZAdydvX05BvMK7qcxbGbXAa8H/jPuqDbd4jyJiMgyNZgvs+voCIWyRmRdSC3rZA94OvAG4F4z+0mc9odEPfreHPfouxd4NYC732dmNwP3E/XA/1Z3r3a9+Bbg40A7Ued66mBPRGSFKJQDDg0WGCmodGAGrwV+BbjG3Q+b2SnA/21xnkREZJlxd44MFTk2rCr5rdDKXvS/Q/328wCXT7HM9cD1ddLvAs6fv9yJiMhiN1Ks0DtcZFiBfUPc/TDw/prXe1EbfBERmUeVIGRvX47RoobAa5UZA3wzeypRdb5nAluAPPAz4D+BT7n7YFNzKCIiEnN3BvNlekeK5Euq8jcbZvZK4H1Eo9NY/HB372lpxkREZFkYLVbY25ejEqib/FaaNsA3s1uJOqz7ElHJ+VGgDTgbeC7wJTN7v7vf0uyMiojIyhWEzvHRIn2jJcoVXTjM0V8BL3P3B1qdERERWV6ODRc5MlTQEHiLwEwl+G9w995JaSPAj+LH35jZ+qbkTEREVrwwdI4OFzk+WiRUgf3JOqLgXkRE5lMQOgf68+rkdhGZNsCvBvdm1gnk3T00s7OBc4Fb3b1c5waAiIjISRstVtjfn6dUUWQ/T+4ys88C/w6M9XxUM0ytiIhIw8pByO7eUfWSv8g02snet4Fnmtka4JvAXUS98b6uWRkTEZGVKQydQ0MF+kZKrc7KctMD5IAX1qQ5oABfRERmpVAO2H18VM3mFqFGA3xz91w8dN2H3P2vzOzHzcyYiIisPMOFMgcG8rpgaAJ3/9VW50FERJa+fCngsd5RglDn6sUo0eD7LO5N/3VEvedDC4fYExGR5SUInX19OXb35hTcN4mZbTezL5rZUTM7YmZfMLPtrc6XiIgsHSPFCo/2jii4X8QaDfDfDlwHfNHd7zOz04H/bl62RERkpRjMl3n4yDADOXXQ02T/AtwCbAW2AV+O00RERGY0mC+zu3dUnd4ucg2Vwrv7t4na4VdfPwq8rVmZEhGR5c3dGSpUOD5SZLQYtDo7K8UGd68N6D9uZr/TqsyIiMjS0Tda4uBAXsPgLQENBfhxz/m/B+ysXcbdn9ecbImISCu5O2Y27+utBCF9uZLGs2+NXjN7PfCZ+PUvA8dbmB8REVkCjg4XODJYnPmNsig02o7+c8A/AP8EqKhFRGSZOzRYIJNKsL4rOy/ry5cCjo8WGciVdfe/dd4MfBj4W6Le8++I00REROo6NJind1ij2iwljQb4FXf/aFNzIiIii0K+FNA3Gp3MM6kEPW3pOa9rMFemd7RITtXwW87d9wIvb3U+RERkaRjMlxXcL0GNBvhfNrPfBL4IjNXPcPe+puRKRERa5sBAbqyUfe/xHGdu7KItnZz1eg4PFjg2rCp9rWZmfxAPb/shopL7CdxdfeqIiMgJBtX57ZLUaIB/dfz8+zVpDpw+v9kREZFW6h0pki+Nd4/rDruPj3LGhi7SyUYHXoEDA3n6RnTXf5F4IH6+q6W5EBGRJSMMnaGCAvylqNFe9E9rdkZERKS1ykHIkaHCiekVZ8/xUU5f30UiMX3He+7O/v68hrxroiB07j842PD73f3L8fONTcuUiIgsK8OFivrMWaIa7UU/DbwFeFacdDvwj+6uKzgRkWXi0EBhyrFt86WQ/f15TlnXMeXy7s6+vjyDeZ0a5pu788ixUb718DH+5+fHOD7aeO0IM/sydarm16xb7fJFRGQCncuXrkar6H8USAMfiV+/IU77X83IlIiILKyhQnnGk/lgvszhwQKbV7WdMC8Mnb19OYYLlWZlcUU6OJDn2z8/xu0PHePAQJ5Uwrj41DVcc/YG3vq+hlfz103MooiILDOqnr+0NRrgP9ndL6p5fZuZ/bQZGRIRkYUVhs7BgXxD7z02XCSTSrC2MzOWFoRRFf7RZdxT/nChzN6+HKlEgnM2dzf1s8pByK0/O8y3Hj7Kw0dGADh/aw+veMI2nn7mOrpnOaqBu3/rZPJjZjuATwCbgRC4wd0/aGZrgc8CO4HdwGvcvT9e5jrgGqKhdd/m7l+N0y8GPg60A18B3u7ubmbZ+DMuBo4Dr3X33SeTbxERmRtVz1/aGg3wAzM7w90fATCz04lO2iIissQdHS5SrjR+Jj84kCeTStCVTRGEzmO9o+RLy+OUUCgH7O3Lsfd4jj19o+w+Hk335carxL/zBWfznHM2Ni0PH79jN7f89CCnr+/kV5+2k2eetYEN3dk5r8/M7mX6KvoXzrCKCvBOd/+RmXUDd5vZ14E3Ad909/ea2buBdwPvMrPzgKuAxwNbgW+Y2dnuHhDV/rsW+D5RgH8FcCvRzYB+dz/TzK4C3ge8ds4bLSIic6bq+UtbowH+7wP/bWaPAgacCvxq03IlIrKCFCsBQ/kKQ4UyQej0tKXpaU/RkWn0J3ruCuWA3pHZDWXnDnuOj7JzXScHB/IUylM03F8ijg0X+cYDR/jWw1E1+KpMKsEpazp4wimrOXVtB6es6+Dzd+/nQ7ftYtvqds7aNP8l+UeHC3zl3kO84HGbeNvlZ83Xan/hZBZ290PAoXh62MweALYBVwLPid92I1H/PO+K029y9yLwmJntAi41s91Aj7t/D8DMPgG8gijAvxJ4T7yuzwMfNjNzVxmSiMhCUvX8pa/RXvS/aWZnAecQBfgPxiduERGZg1ypMhbUFycFyMfKRY4NF0klje62FKva03RlU5hN34P9XOzvz8+pGl4YwqPHRuc9PwulEoT8cHcfX73/CD/a048DT9ixmuees4FT1nVy6toONvW0kZw0asBZG7v53Zt/wvVfeYD3v+YJE5oqzIfP/nAfAFddumPe1unue+ZrXWa2E3gi8ANgUxz84+6HzKxarWEbUQl91f44rRxPT06vLrMvXlfFzAaBdUDvpM+/lqgGAKeccsp8bZaIiMRUPX/pmzbAN7PnufttZvbKSbPOMDPc/d+amDcRkWXD3RkuVhjKlxkuVKgEM589K4HTP1qmf7RMIgHd2ahkP5VMELrjHq3Xneg10TNAZyZFRyY57U2BvtHSsqla36iDA3m+dv9hvvnAUQbyZdZ1ZnjNk3fw/MdtYnPPiZ0HTraqPc0fv/Rx/P7n7+EvvvIAf/GLF5BJJeYtb9944AgvuWALG7tnzkujzOw77v4MMxtmYlV9A9zdexpcTxfwBeB33H1ommOr3gyfJn26ZSYmuN8A3ABwySWX6BJURGSeqXr+0jdTCf6zgduAl9WZ54ACfBFZstydXCkgXw5IJxJkUgmyqcSMY703Kgid4UKZoXyF4WJ5yiHoGhGG0Um38RNvETPoyqbozKbobkvRlk6Oza0EIYcGG+tYbykoVUJypcrY/zMfP+dK0XSuVOGHu/v42cEhEgaXnraWF563mSedsuaEUvqZnLa+i3c8/2ze+18P8pHbd/H2y8+al9oV//qDvaSTCV5z8fyV3gO4+zPi5zm3KYiHy/0C8K81N/ePmNmWuPR+C3A0Tt8P1G7EduBgnL69TnrtMvvNLAWsAvrmml8REZk9Vc9fHqYN8N39T+JntbcXkWWhUA4YKVYYKVQYLVXqBt2ppI0F+9FzkkwyQTJhpBI27Q2AShAyVIhK6keKra3m5h5VtRsuVDg8CMlEVOW/M5tipFB/2xejIHSOj0bNFo4NFzk2Mj7dG0+PNlATYcuqNt741FO5/NxNJ121/ulnrueXn7yDz/xwHzvXd/KKJ2ybeaFp7O4d5X9+foxfetJ21sxztf9aZraGKJAeO/+7+49mWMaAjwEPuPv7a2bdAlwNvDd+/lJN+qfN7P1EneydBdzp7oGZDZvZZURV/N8IfGjSur4HvAq4Te3vRUQWlqrnLw8zVdH/3enmTzrRi4g0pBKEjJYC3B0zI2GQMMMmPSdt+mC64c8qBgwXo4C7kd7iK4FTCQJyUwz7ZhbdBEgljGQiET9bVGK8iIeKC0JnIFdmILe47867OwcHCty1p4+79vTzswODVMKJ/7fubIoN3Vk29bRx/tZVrO3M0JFJ0p5J0p5O0pFJ1UyPp89nPwZXXXoKu4/n+JfvPsYpazp40qlr5ryuT/1gD+2ZJK980sndKJiOmf0ZUc/3jxINdwdRbbznzbDo04E3APea2U/itD8kCuxvNrNrgL3AqwHc/T4zuxm4n6gH/rfGPegDvIXxYfJujR8Q3UD4ZNwhXx9RL/wiIrKAVD1/eZipin5zB/sVkRWhWn16pBhVoZ7cqdx02tIJ2jNRwNaRSU6oZl5PoRwwGn9OrhRQqsx/MbU7lCtOOWr1Pu/rX4mKlYB7Dwxy9+5+7trTz+GhAgA71rTz0gu2sGNtBxu6s9GjKzvjcbAQEma84/ln8wdf+Cl/9bUH+ZtXPYFta9pnvZ6Hjwzzg8f6eN1TTpn1GPez9BrgDHcvzfjOGu7+Heq3kQe4fIplrgeur5N+F3B+nfQC8Q0CERFZeKqev3zMVEX//yxURkRk+ShWAkaLUaA9Wmqs1HwqhXJIoRzSPxqddBIJaE8n6cxGJbQG5EsBo3E766VS7XwlKwchR4YKHBjIc6A/z70HBrln/yClICSTSnDR9lX84hO3cfGpa9jUQMd3rdSeSfJHLz2P3735J/zZf97P37z6Ijqzsxve8JPf30NPW4qXX7S1Sbkc8zNgNeNt5UVERABVz19OZqqi/3fTzXf3t81vdkRkKSrEnZmNFqNS+kZ6iJ+rMCS+ebB4q8JLpByE3H9wKArkB/IcjJ+PDBWorXG/ZVUbL3r8Ji45dS3nb1s1b73SL5TNPW1cd8W5/O9b7uOvv/YQf/zS8xruuO/e/QP8ZN8A1zz9NDoys7sxMAd/CfzYzH4GjA116+4vb/YHi4jI4jaQn1XlLlnEZrqauHtBciEiS4a7U6yEY9Xgmx3Qy9J0aDDPX3zlAXYfzwGQSSXYtrqd0zd08ayzNrB1dTvbVrezdXVbs6ulL4gLtq/m2meezke/9QgfuX0Xv/6sM2a8UeHufPL7e1jbmeHFF2xeiGzeCLwPuBe1LRERkVgYOsOFSquzIfNkpir6Ny5URkSk9YLQqYQh5cCpBCGlYHy6HISUKk4QKpiX6f1wdx9/8/WHMIzfe+E5PH5rD2s7MyTmsYO7xeglF2zh6HCRL/xoPw8fGeadLziHnes7p3z/3Xv6eeDwML/5nDPIphakT4Fed5+2Zp6IiKw8Q4WyqucvIzNV0f+Au/+OmX2ZqKfdCVStT2TpKAch+XLU6VwliAL56Hl8Wj/ucjJCd266cy+f+eE+Tl/fyXUvfhybVy3uNvTz7U1P28n5W3v44Dd/zjtu/glXP20nL79o6wk3N8K49H5zTxsveNymhcre3Wb2l0RD0tVW0Z92mDwREVne1Hv+8jJTFf1Pxs9/3eyMiKxk7lGgHYRO6PFzCJUwJPBoOnQnmTDSyQSppJFOJEgnjVTyxGrApUoUzBfKAflSQL4cqBq9NNVwoczffP1h7t7Tz/PO3biQpdKLziU71/KhX34iH7ptFx/7zmPctbuPdzz/bNZ1Zcfec8cjx3m0d5R3PP/sut/hJnli/HxZTVojw+SJiMgyper5y89MVfTvjp+/tTDZEVn+ykFIoRzEvcMHFCvR9FxLz6tjsqeTCYyo13lVo1/cKkHI0eEih4cKHB4scGiwwJGhAocG8+RKQTxue4rOTLLudFc2GkWgM5OiK5uKprPzP857ox49NsJf3PoAx0dKvOXZZ/Di8ze3JB+LyeqODH/80sfx1fuO8E/feZTf+syP+a3nnsnTz1xPEDr/+oM97FjbwbPP3rBgeXL35y7Yh4mIyJKg6vnLT0Nd9prZLwB/BpwaL2OAu3tPE/MmsiSEoRP4iaXvoXtc+h6VzleD+vkOvsfGZK8snl7lqzcxgrhWQrUWQlCzr6r7q1pzYfwRxn0BOAkz1nVm2NCdZc08teEOQmcoX6Y/V2IgFz2PlgK6skl62tL0tKfj59RJBcyFcsChwQKHB/McioP4Q4N5Dg8VODZcnNCLfDppbO5pY/OqNrqzaXLlqAPDgXyZgwN5cvEoBaXK9P2iJQw6syl62tLsXN/JWRu7OHNjF2du6Jr10G2Nuu3BI/z9fz9Cd1uKv3zlBZy7WaeFKjPjivM3c8G2VfzN1x/ivf/1IM87dyNnbuhif3+e6158bsO97Z9kPl4PfNrd6x5AZnYGsCUe715ERFYQVc9ffhq94vsA8ErgXnfd45GZuU8K7MYC3yioq6oGTzb2On5mbGJS+vhy7pMC6ZrXoddvT2428TPNIGGGGSTNSJiRSBiJOD2ZMEJ3yoFTDqJ26uUgpBJGz+UgXLbjrld7y69W9S+UA/LlkEIpYLRUYShfZiBfZjBfZiAXPQ/mywzkS00Zwi6VMNZ3ZdnYnWVD/NjYnaUjk6JYCShWQorlkEIloFgOx9IK5ZChQpmBOKAfzJdP7FBkms+sBvsdmRTpuKZEJpWInuPmEtFzgv5cKS6Rz9Ofm3jC7G5LsWVVG+du7uE5Z0fB/OaeNrasamv45kUlCKPhCEsVRovRCAbVoQmrzyPFCgO5MruODvPdXb1jy25b3R4F+xu7OGtjF2ds6KItPfcq9L0jRW6+ax+3/uwwF2xbxe+/6BzWdGTmvL7lbNuadv7qly7kprv28bm79nHbg0c5c0MXTz193UJlYR3R8Hh3E42OcwxoA84Eng30Au9eqMyIiMjiEKh6fkv0j5b4y1sfaNr6Gw3w9wE/W8zBvZldAXwQSAL/5O7vbXGWFrVgUnvvaklz6IwFx+7RaydOj4scw2p6zbNX00OmDK5lXDkO1HKlSvwcVdUf77F+4o2Eaqd41Tb4mdrgMhVNp5MJzBhb70gxeh4tRgFhrlhhtBQFvdVS8upjcil6FBgHMwbCRhS4rurIsLo9zWnrO1ndvppVHWna00lSieiGSTJhJOMbJtVHwmxsfqomPWlGKhnNDx2OjxQ5Olzk2HD1ucBP9g3QN1qaMn+phJFNJcimkmTTCXra0mzqaeOczT2s7kizJs7vms7ouTObIleqMJSvMFQoM5QvM1wYnx4qRAF0qRIyWgzi0QWqIwuM/7962tNsWdXGxaeuYcuqdrasamPLqnY2r2qjax5K0FPJBD3tCXraGxtWbihfZtfREX5+bIRdR4e57+Ag33r42Ng+umDbKi7ZuZYn74zyO5PekSLf3dXLd3f18sDhYQBe8YRtvOlpOxekJHopSyUTvP4pp3LxKWv41Pf38PrLTl2wZgzu/kEz+zBRW/unAxcCeeAB4A3uvndBMiIiIovKsKrnL7iHjwzzF195gJFi826sWCMxu5k9maiK/reY2PPu+5uWs1kwsyTwMPACYD/wQ+CX3f3+qZZ54pMu9q9/+45JVYPHg6pSJW4TbR6V+lJT0hz/qb008zgQrkmp5o5UtWO0hJFKJUgnElEJcRzk1AbKlXB8eLIgiDtZC2vWbNFnWfwZFr8+MUcTc+JUt88JgvES7hOCdcbTo20anwdOUFNCHtTcEAhDn9AZ3ITq6j5ejb3ecg5jJftjNxjGNzcuaR/ftuj/wdh7xz9rcl483vboT3Wt1e2qqgabybjkPmk2IQ0m76eJN0LCmv9bdR9Xe6UPQqccOuVKyGipQj4O5kvBwhX7Z1MJOjNRG+2OTIpsOjoWE3EgHW1vgmQCUokEiYTRlkrQlonadLelk7SnE7SNTUePVR1RVfZWBXaVIKR3tEShFNCWTkYBfToxVpou9fWPlvj50RHuPTDIXXv62N+fB2D7mnaevHMtTz51DY/b0jO2D48NF/nuI1FQ/2Ac1J+2vpOnn7meZ5yxnm1rZr4xIM1x4Y7Vd7v7Ja3OR6tccsklftddd7U6GyIii1IYTrqeHqsVO37dtuf4KEN5leAvlNsePMKH/3sXazoyvO+XLuB5j9vclPN4o0VK1wMjRFX6FmMdzEuBXe7+KICZ3QRcCUwZ4D90ZJjn/vXtk9pPs2Cdk1WrgCfMJpSQ6yZaxKgzLuMUxgLzalBeU81+cnX86q2R6k2C6s2FYNJNirHXoY9V60/UPFer9Vf/h8nExBs5ybiX+6j6ttGZSbN1dRsdmRQdmSQd2RQd6eRY0N2RiYLnVCLqFT+dNFI1veRXXwehj5ceV8bHqa+WJIfudNQE852Z5LINdlPJBJt7VtYQbPNhTWeGS09by6WnreWaZ5zGocE8P9zdzw939/Hlnx7kiz8+QGcmyRN2rKZ3pMRDR6Kg/vT1nbzhslN5uoJ6ERGRRakShHFzyTK5GZpL1hYSSnMFofPP332MW356kAu2reJdV5zLzvUdTfu8RgP8te7+wqbl4uRtI2pGULUfeMrkN5nZtcC1AN1bTuNZZ22oKakdb3NdTTuhPTgntgU/8TMmvo5KeKulvfFNhGrpskOIY0wMFMeC/5ogdULA69Unn/A5M5kYlEY1CIzx9ufUzKt+ZvX90fLVALcmn/G+mhBgTy4Rr3k9OfiuvdExnqeJdxertUy8ZjvdfULQvVIkE1GVfJH5smVVOy+/qJ2XX7SVXKnCT/cN8MM9/fx4bz897WneeNmpPP3M9WxdraBeRESkmSpByLGRIqPFylityY5MirZ0Ysrr3UoQMlSoMJArkSsFDQftCu4XxlC+zPu++iD37B/kZRdu4c1PP63phW+NBvjfMLMXuvvXmpqbuat3xJ9w2Lr7DcANEFXt+8BVTzihqnrojofjwbMRBb5jnbHBWJX9et+zqTt2G1++GmjX+6L6pKrxE4JaJrZt9zqB/lR5qO6QIBy/0VDbGd3Y9oc+YR3Vz6yXn/G1Ts7PxOrrUXv++nk6Yf/U7KfqvDpLjH32eN5q8lozPaF5hU3839WW6o+/PvF/XO1kL2qrvnw71RPpyKR46hnreeoZ61udFRERkRXD3ekdKXF0uDB2nZkvhfQTddhrBm1xc8lqwF8oR6X1o8WKgvVF6rHeUf78P++nb7TE2593Fs8/b9OCfG6jAf5bgT8wsyJQZvENk7cf2FHzejtwcKaF0ouw6rJZ7Y2D5VUyXdvXwFIuffdJAX+1eUd1Hozf9qitcTA+kkDUt0K134BqHwKz+XGuvSmSnFTTY7xPAHV2KCLjzGwT8BfAVnd/sZmdBzzV3T/W4qyJiKxYA7kSR4aK0w6F6x4F/PlSSP+ohrVbCr6zq5cPfONhOrMp3vvKCzlnc/eCfXZDAb67L1yO5uaHwFlmdhpwALgK+JXWZkkmMzOSNe3glyozI5OqbsPchxmbzGuCfK9Jq31tRNX0G70xEtb0kF+p7Tk//qzqa/fxoQxra2rUqxUBqtYlskR9HPgX4I/i1w8DnwUU4IuILLDRYoVDgwXypfkfWlhaZyBX4nN37+eWnx7k3M3dXPfix7G2c2G7sJs2wDezne6+e5r5Bmxz9/3znbHZcPeKmf0W8FWiiOuf3f2+VuZJZLYm1t4YSz2pdSYSRibRnJsRwYSbBxObMFTCqNO/2bQFE5GmW+/uN5vZdTB27tSVpYjIAiqUA44MFdR7/SISujOYK3N0uMhgvsTZm7pZ3TG7oPzoUIEv/vgAX7v/COUg5MXnb+bXnnl6S2qMz1SC/3/NLAF8CbgbOEbUk/6ZwHOBy4E/Iaoi31Lu/hXgK63Oh8hKYPEQe6kZ7hm4O8U40M+VKhTKAYVy2FinkImotoJqEIjMm1EzW0dcKcjMLgMGW5slEZGVYbRYoXekyHBBbeZbZX9/jgcPD3NsuMjR4UL8XKR3pEg5GP+nJAwev3UVTztjHU89fR3rurJTrnNfX47P/2g/33r4GADPO2cjr3zSNravaV4v+TOZNsB391fHbfReB7wZ2ALkgAeIgunr3b3Q9FyKyJJkZrSloyEAq9WTwtDJlwPy5ajgMJWIRlZIxaMtpOKRLGZqhlBt0lCJhw4slgOKlajmQLESDSW4HE+gGtZGTsLvArcAZ5jZd4ENwKtamyURkeXL3RnKVzg2UlRV/Bb71sPH+MA3HqYSRiOYrenMsKEry5kbu3jaGevY0JVlQ3cbndkkP943wB2PHOcfv/0o//jtRzl3czdPO2MdTztjPZviIZp/fmSYz929n+8/epx0KsFLL9jCK56wjQ3dU98MWCgztsF39/sZb68nInJSEgmjM5uiM9toH5/1VZs0ZOKhA7smrc89DvwrIZXAqQThWOeDlXB8OggXb7Rc7TW3PZOiPR4upy2dwB2KlZBiJbqpUSyPTy+m4L8zG93YKQUh5cApxTdeSossnyuFu//IzJ4NnEPU/uchd1dvTSIi8ywInb7REsdHi5QrOuG1krtz8937+dT39/D4rT381nPPZFNP27RV5x+/dRWvf8qp7OvPcccjx7njkV7++bu7+efv7uaMDZ10ZlPcs3+QzkyS11yyg5ddtJVV7ekF3KrpndwVtojIImVmZFNJsjO0I6j2JzB5SEqoX1I+eajF2mEvJw9BGYY1086E+T7pdTUtkbAJwXy9mgxm0J5J0p45cduqgX61JkOp5rFQkglj6+q2aduvVQP9chz813YEOdanwyK/AbPUmNkbJyU9KT5uP9GSDImILDPlIKR3pEjfaEnDKi8C5SDk7/97F9988CjPPWcDv/28s2bVJn7Hmg5ee0kHr71kB4cHC9zxSC93PHKcw4MF3vS0nbz4/M10ZBZfOL34ciQisoCq/QnM17qSBskWjhQx1U2Nan8I1aC/WaUKa7sybO5pI5mYfh+kk4mGT7KVIIyadZSCuD+HQIH/3Dy5ZrqNqB+dHwEK8EVETtJQocz+vrzOT4vESLHCX976APfsH+RXLj2Fq56846SG5968qo1XPmk7r3zS9nnMZXMowBcRWQFq+0OAqJT9QH9+3tbflk6wbU17U+5kp5IJupMJutvGq78VK7UBf6XhzhtXMnf/7drXZrYK+GSLsiMisiy4O0eGihwbLrY6KxI7PFTgT798H4cGC7zj+WfzvHM3tjpLC6qhKzEz+6a7Xz5TmoiILA1rOtIcGSpQCU4uKjaDTT1trO/KnNSd8dmq1lRYHXdSmytV2N2bU8nJ7OSAs1qdCRGRpapUCdnXnyNXVAd6i8VDh4f5s/+8nyB0/vTK87lg26pWZ2nBTRvgm1kb0AGsN7M1jA/K3QNsbXLeRESkScyMdZ0ZjgzNvcShM5tk+5oOMqmFH+N1so5MijM2drLneI5iWQ0f6zGzLxMPkQckgPOAm1uXIxGRpUtV8hef7+7q5f1ff5i1nRn+5GXntXSoulaaqQT/14HfIQrmf1STPgT8fZPyJCIiC2BtZ4ajw8U5VW03Y9EE91XZVJIzNnSx+/ioSlPq++ua6Qqwx933tyozIiJLkarkLz6FcsBnf7iPL/xoP+du7uaPXnreourVfqFNG+C7+weBD5rZb7v7hxYoTyIisgBSyQRrOzMcHynNetn1XdlFFdxXJRPG6es72d+fZyA3uxHgzOqPnLBcuPu3Wp0HEZGlTFXyFxd35/uPHuf/fecxjg0XecF5m/iNZ52xKK9PFtJMVfSf5+63AQfM7JWT57v7vzUtZyIi0nTrujL0jZZmFdgmE8aG7mzzMnWSzIwdaztIJwsNlbB0taVY25mhpy3F0eEiR0+i2cJiFp/H3wdsJGpyZ4C7e09LMyYissiVg5D+0RK9IyVVyV8kDg7kueF/HuXuPf3sXNfBO195AY/fuvLa29czUxX9ZwG3AS+rM88BBfgiIktYNpWkpy3NYL7x0u4N3dkZh8FbDDavaiOTSnBwIH/CDYxEAtZ0ZFjbmRkbWQCiDgM7Mkn2Lc92lX8FvMzdH2h1RkREmq0chIwUKowUo0dbOklPW4qe9nTDw7QOFcr0j5YYLlSWdQ2vpaRYCfjc3fv5wt37SScT/NozT+OlF2xdEtclC2WmAL8/fv6Yu3+n2ZkREZGFt6E723CAn04Z67syTc7R/FnbmSGdNPb25QhDyKYTrOvMsLojM+XFQHdbmrM2JdnXl2N0eVXDPKLgXkSWK3cfC+ZHCtHwqbVGgij94ECB9kySnvYUPW3pCTd5Yby0vi9XolxRVL+Y3PnYcf7x249ydLjIs8/ewJuffhprO5fONclCmSnA/1Xgg8DfAU9qfnZERGShtWeSdGaTDQWzm7rbFnQ4vPnQ3ZbmjA1dVEKnK9vQ6LCkkwlOW9+53Krs32VmnwX+HRjbKDW3E5H55u4M5su4w6r2NIkmla4GYfQ5Q/kyI8XGS9nzpYB8KeDIYJFsOkFPW5r2dJKBvErrFxN358BAngcPD/PdXb3ctaefHWs7+ItXnM8F21e3OnuL1kxXOg+Y2W5gg5ndU5Nebbd3YdNyJiIiC2Z9d5bRYm7a97RnEqxZonfKJ5fQNMLMlluV/R4gB7ywJk3N7URk3lSCkL7REsdHS1SC6Dfz4GCeVe1p1nRk6GzwJutMhgtlBnLlsZsIJ6NYDjlWXjY3cpe0fCng4aPDPHh4mAcPDfHQ4WGGixUAurIpfvVpO3n5RVtJNdjEYqWaqRf9XzazzcBXgZcvTJZERGSh9bSlyaYT044hv6mnbQFztHgslyr77v6rrc6DiCxPhXJA70iRgdyJAXcYQv9omf7RMtl0gjUdGVZ3NN4OvvYzBnJlBvKqOr+cHB0q8B/3HuKn+wbYfXyU6r30HWvauez0dZy7pZtzN/ewfU07iSVWg7BVZryN5u6HgYvMLAOcHSc/5O6zG39IREQWtfVdWQ705+vO62pL0d22cseUrVbZf6x3dMkG+WZ2NvBRYJO7n29mFwIvd/c/b3HWRGSJGiqUOT5SYqRQaej9xXLI4cECR4YKdLelWN2eIZmMgjZ3pxq2j90kcKiEIf25MvnS0vztlfp2HR3hiz/ez3d29WJmnL+1h1dfsoNzN3dz7qYeutrmp7bHStTQnjOzZwOfAHYTVc/fYWZXu/u3m5g3ERFZQGs60hwZKoxVq6y1eYWW3tcyM7raUks2wAf+H/D7wD8CuPs9ZvZpQAG+iMxKoRywry93Qkd2jXKHoXyFoXxjNwZkeQjd+dGefr744wPcc2CQ9nSSK5+wjZdduHVRD7+71DR6a+T9wAvd/SEYKwX4DHBxszImIiILy8xY15XhyODEtoirO9K0Z2bfhn056sikqOmfbqnpcPc7J3WSOOPVtZn9M/ALwFF3Pz9OWwt8FthJdPP/Ne7eH8+7DrgGCIC3uftX4/SLgY8D7cBXgLe7u5tZlqgQ4WLgOPBad999ktsqIk0ykCuxv//E4UdFplIOQr710DH+7ScH2NeXY31Xhjc/fScvPG/zvPXLIOMa3aPpanAP4O4Pm9nKraspIrJMrevMcnSoOHbhZgYbe3RXvap9Dp31LSK9ZnYGUcd6mNmrgEMNLPdx4MNEQXjVu4Fvuvt7zezd8et3mdl5wFXA44GtwDfM7Gx3D4iaB1wLfJ8owL8CuJXoZkC/u59pZlcB7wNee7IbKyLzy905NFjg+Eip1VmRJeLQYJ7bHjzKV+87TH+uzGnrO/ndF5zNM89cr47ymqjRAP9uM/sY8Mn49euAu5uTJRERaZVkwljbmRm7gFvbmSGbWtJB7bxKJmzGzggXsbcCNwDnmtkB4DGi8/m03P3bZrZzUvKVwHPi6RuB24F3xek3uXsReMzMdgGXxiPy9Lj79wDM7BPAK4gC/CuB98Tr+jzwYTMzd5UPiiwW5SBkb1+O3NJtoiQLJFeq8J1dvdz24FHuOziEAU88ZQ2/+8RtXLR91ZIbancpajTA/w2iC4O3EbXB/zbwkWZlSkREWmd9V5a+0VJUeq82cSdoTyeXaoC/x92fb2adQMLdh09iXZvc/RCAux8ys41x+jaiEvqq/XFaOZ6enF5dZl+8roqZDQLrgN7JH2pm1xLVAuCUU045ieyLSKNGixX29uXq9s8iAhCEzr0HBvnmA0e449HjlCoh21a388bLTuW5525kfZeuJRbSjAG+mSWAu+N2d+9vfpZERKSVMqkEPW1p2jIJVaGroyOTZCC3JAeSeczM/ouo7fxtTfqMekUzPk36dMucmOh+A1EtBC655BJFGyJN1jtS5PBgQe3tpa6jQwX+677D/PdDR+kdKdGZTXL5uRu5/NxNnL2pS6X1LdLIMHmhmf3UzE5x970LkSkREWmtjT1ZMgru64o62muu1R1pBvMnjid9ks4BXkZUI+9jZvYfRNXpvzOHdR0xsy1x6f0W4Gicvh/YUfO+7cDBOH17nfTaZfabWQpYBfTNIU8iMk/C0DkwkF+qNzOlidydnx0Y5Mv3HOIHjx0H4EmnrOHNTz+Np5y2jkxK1w6t1uhVyhbgPjO7ExitJrr7y5uSKxERaam2pd2ZXFO1pROY0dQSrfVdWTZ0Z9nbl5u35gDungduBm42szXAB4FvAXP5Z98CXA28N37+Uk36p83s/USd7J0F3OnugZkNm9llwA+ANwIfmrSu7wGvAm5T+3uR1jnZIfBkeSqUA7718DH+456D7D6eo7stxS89aTsvPn+LhrhbZBoN8P9PU3MhIiKyRJgZbekk+VLzOptKJY10MsGZG7rmtRTNzJ5N1EP9i4EfAq9pYJnPEHWot97M9gN/QhTY32xm1wB7gVcDuPt9ZnYzcD/REHxvjXvQB3gL48Pk3Ro/AD4GfDLukK+PqBd+EWmBvtESBwc0BJ6MOzpc4Cv3HuZr9x1muFjhtPWdvO15Z/KsszeoE95FatoA38zaiDrYOxO4F/iYu884Zq6IiMhy1pFpXoBvBum4eUQiYexY20Fn9uQvus3sMeAnRKX4v+/uo9MvEXH3X55i1uVTvP964Po66XcB59dJLxDfIBCR1ghC50B/nsG8quRLxN35yO2P8LX7DwNw2enreNmFW3n81h61rV/kZirBv5Go59v/Ibrbfx7w9mZnSkREZDHryCQ53qR1p5InXjit7czQkUmebJX9i9x96KQyJyLLTq4U9ZJfrqjYXsbdvaef/7rvMC88bxOvffIONna3tTpL0qCZAvzz3P0CADP7GHBn87MkIiKyuLVnmlctMZWo30FRWzp5slX2N5vZF4mGuDvfzC4EXu7uf34S2RWRJezocIGjQ0VVyZcJQnc++f09bO5p4y3PPkMj6iwxM/23xq4gVDVfREQkkk0lSSaaU0UxXacEv6paZX/r6jmVpPw/4Dric7u734Pau4usSJUg5LHeUY4MKriXE313Vy+P9o7yuqecouB+CZqpBP8iM6tW5zOgPX5tgLt7T1NzJyIiski1Z5KMFOb/3ncjF1PruubUY3GHu985qe2kbt6LrCBh6PTnShwdLlIJFNnLiYLQ+dT397BzXQfPOntDq7MjczBtgO/u6hpRRESkjo4mBfjpJtUMAHrN7AzAAczsVcChZn2YiCwehXJA32iJ/lyJUKPfyTS+8cARDg4W+OOXPo6EOtNbkhodJk9ERERqNKsdfrp51SHfCtwAnGtmB4DHgNc168NEpLXcnaFChb7RUlNuRsryU6qE3PTDvZyzqZtLd65tdXZkjhTgi4iIzEFHujkBfr1e9OeDuz8KPN/MOon64MkDrwX2NOUDRaQlKkFIX65E32hJPePLrNz6s0P0jpR4x/PP1lB4S5h6TRAREZmDVDJBJjX/p9H5LsE3sx4zu87MPmxmLwBywNXALuA18/phItIyhXLAvr4cDx4e5shgUcG9zEquVOHmu/bxhB2ruXD76lZnR06CSvBFRETmqD2dpFSZ3watqflvg/9JoB/4HvBrwB8AGeAV7v6T+f4wEVlYQ4UyvcNFRotBq7MiS9gtPz3IUKHCGy47tdVZkZPUkgDfzP4v8DKgBDwC/Kq7D8TzrgOuAQLgbe7+1Tj9YuDjQDvwFeDt7u5mlgU+AVwMHAde6+67F3J7RERkZWrPJBnMz2lM+rrMGutFf5ZOd/cLovXbPwG9wCnuPjzfHyQiC6PaG/7x0RLFsnrNk5MzlC/zxR8f4Kmnr+PsTd2tzo6cpFZV0f86cL67Xwg8TDQuL2Z2HtGYvI8HrgA+YmbVRo4fBa4FzoofV8Tp1wD97n4m8LfA+xZqI0REZGXrmOeO9prU/n7sDoS7B8BjCu5FlqZSJeTwYIEHDg9xcKCg4F7mxRd+tJ98KeB1Tzml1VmRedCSAN/dv+bu1e48vw9sj6evBG5y96K7P0bUPvBSM9sC9Lj799zdiUrsX1GzzI3x9OeBy029QoiIyAJoTyeZzzNOk3rQv8jMhuLHMHBhddrMhprxgSIyv4YLZfYcH+XhI8McGy5qqDuZN8dHivzHPYd47jkbOXVdZ6uzI/NgMbTBfzPw2Xh6G1HAX7U/TivH05PTq8vsA3D3ipkNAuuIqiCKiIg0TSJhtKUT5Evzc7WdTsx/gO/uzenuX0SaKoir4fepGr400Wfv2kfozi+r9H7ZaFqAb2bfADbXmfVH7v6l+D1/BFSAf60uVuf9Pk36dMvUy9O1RNX8OeUUHcQiInLy2jMp8qXSvKyrWUPkicjSkS8FHB8tMpAr4+oIX5ro0GCer91/hBc9fjObe9panR2ZJ00L8N39+dPNN7OrgV8ALo+r3UNUMr+j5m3bgYNx+vY66bXL7DezFLAK6JsiTzcANwBccskl+skUEZGT1p6evwJyBfgiK0slCCkFIaVK9BgqVMiX1Bu+LIxP37mXZMJ47SU7Zn6zLBmt6kX/CuBdwLPdPVcz6xbg02b2fmArUWd6d7p7ELcVvAz4AfBG4EM1y1xNNPzPq4Dbam4YiIiINNV8drTXjCr6ItJ6QegM5stjgXwpCChWQrWll3lXPdYG82XcHYcTaoK4R80/vvXQMV75pO2s7cy0JK/SHK1qg/9hIAt8Pe4P7/vu/hvufp+Z3QzcT1R1/61xj78Ab2F8mLxb4wfAx4BPmtkuopL7qxZsK0REZMVrSydJJJiXC/V0SgG+yHLi7vSOlDg2XCQIVf4kJxotVjgwkOfgQH7seaRYIZtK0pZO0JZORo9UNJ1NJ8mmEuRKAQNxHw39uTL9uRL9uRJD+TKNHmqdmSS/9KRtM79RlpSWBPjxkHZTzbseuL5O+l3A+XXSC8Cr5zWDIiIis9CeTjJaPPlqtamEquiLLAdRCWmZo8MFyhUF9ivdSKHC4aECR4YKHBoscHAwz4H+KJgfyI+NZIoBG3uydLelKVZKFMsBhXJAIa75MVkyYaxuT7OmM8P6rgxnb+xidWeGtR0ZVrWnScTnFIOxEV+s5tO2rm6juy3dzE2XFlgMveiLiIgsaR2Z1LwE+E0aJk9EFtBgrsyRYY1Rv9L050rsOZ7j0GCew4NRMH84fkw+P6zpSLN1dTtPPm0t21a3s3V1O9tWt7O5p43MFDW5QneK5ZBCJaBYDmnPJOluS5HQ6OAyiQJ8ERGRk9Q+D+3wzaLSGBFZmoYLZY4MFdVJ3jJXDkL29eXYfXyUx3qj5929oxNK4tNJY2N3G5tXtXHu5h429WTZ3BO93tTTRkdm9iFYwoz2THJezjeyvCnAFxEROUnz0ZO+Su9Flp5yEDKULzOQL5Obh1o8Mn/KQchIocJwscJIsUJ7OsHq9gw97ekZb6ZWm1gcGoyq0R8aLHBwIM++/jz7+3NjbdwzyQSnrO3gkp1rOG19J6eu62T76nbWdGZUsi4towBfRETkJGVSCVJJoxLMva2thsgTWRoqQTjWS/l8NM1ZLvKlgCNDBY4Mx9XTBwu4w9rODGs7M6zryo5Nd2aS2CwC4EoQDSE4mC8zFO/7wXyZwUL0eqhQYbhQHgvohwtlCtM0kejOpljVkWZVe5rV7WlWdWTIphIcHSpwcLDAocH8hOWTCWNTd5atq9t5ymlrOW19JzvXd7J1VbtqXsmiowBfRERkHnRkkgzlK3NeXkPkiSxetUF9rhScMOzYSuHuHBku8vMjwzzWOxq1MY/bmw8VJv7+tccjjNS7CZJNJcaCfQMqoVMJnSB0KkE4/jpwikEw5Y0UA7rbUnS3pelpS7GuK8PO9Z10Z1N0t6XoakvTnU3RmU1RKAdj/8OBfJnBXImBfJk9fTkGDwySLwVs6mljy6o2Lti2ii2r2tiyqp2tq9vY0JUlpVpWskQowBcREZkH7Scb4KdUCiSyGBQr0Rj1hXLUmVmxElAohysyqB/Ml/n5kWEePjLMw0dH+PmR4bFAPpkwNnZn2dTTxlPPWD/WznxT/OhpS2FmFMoB/bkSx0eiId36RkscHy3RN1qkb7QERMONJhNGqvpIJsZeZ5IJetrT9LRHJe61j65sSiXoIpMowBcREZkHUadJxTkvn1IJvsiCCkMnVw7IlSorPpAHCELnsd5R7j80xIOHh3j4yDBHhqLfNANOWdvBU05bx1mbujh7Uzenru1oqFS7LZ1ky6p2tqxqb/IWiAgowBcREZkXJ9vRXlpt8EWaqlgJyJcCRksB+VJlRQfzACPFCg8dHuaBQ0M8EAf01Xbn67synLOpm5ecv4WzNnVzxobOOfX8LiILT99UERGReZBMGNl0Ys5jX6t9p8jUattlj00HjhNF6EZ0g6zab1vt7bJ8OWrDHYQrL5p3dwbz5bG28oeHChwaLPDI0RH29uVwIGFw2vpOnv+4TTxucw+P29LDhu5sq7MuInOkAF9ERGSetKeTcw/w1Y5UVrBKEFKsVB8BpUpIqRKOdby2kkvap+PujJYCjg0XODpc5OhQkaPDRQ4P5ePO74rkyxM7qFvXmeHUdR0846z1PG5LD2dv7NbY6iLLiAJ8ERGRedKRSTKQK89p2YxK8GUZCEMndCfwKCgPPQrQQ4+C0TBOC0OfENCHc7svtuyVKiHHR4v0jpQ4PhI9HxspcnSowLHhKJifHMBnkgk2rWpjc0+WC7evHusZvtoBXial3xqR5UwBvoiIyDyZaxvVRAISKsGXRSgMnVIQUg6iEvVy4JSDsCZwd4KQsWmVtJ/I3ceC8WIlpFTdl5WQYjD+XKqEDOXL9I4UOT4aBfSTh54D6Mwm2djdxuZVbVywfRUbu7Ns6G6Ln7Osbk/Paox5EVleFOCLiIjMk7Z0AjNmHeSkVXovC8DjoDxwJwyJnuPS9Nr0clxdvhyEVAJF7I1yd3pHSuzty7G3bzR+zrGvL39CKXs9CYPutjTrOjOs78pw7uZu1nVmWNeVZX1XNp7OqLM7EZmWfiFERETmiZmRTSXGeqJulNrfy2wFYVSSHsSdzZXDeDqcGLAH4fhDpesnJwidgVyJ3pESvSPFmkeJY8NF9vXnyJXGA/nVHWlOWdvB5Y/byClrO9jc00ZbOkkmlYgeyeg5m0qQTiZIJUwl7yJy0hTgi4iIzKNsKjnrAF8l+K1T2y68GgSH1ZJtj3plT5qRTBiJ+Dk5xQ2Zagl5Zazq+nhJefVzaj/PiarANxJ4Oz7Wc3w5WNnDu82W+3h7/0I5oFAOaqajPgBypYDRYoVcORpKL1eqMFoMyJej6f5cmb7R0gk98WdSCTZ0ZVnfleG550SBfPXR055u0RaLyEqmAF9ERGQetaUTDOZnt0wqqVK72XKHXKlCuRKVXlerk5eDqJ14JWwsCJ5roJxIMBb0q4R8dtyjmxWFchRAF8phzXQUYBfKIYVKQDEOxouVkGI5oBB3ylcs1zQjiG+qBLXTNcPpFSuN33Azos4y2zMpOjJJOjJJOjMptq1uZ31cVX59V5YN3RnWdWbpbkup1F1EFhUF+CIiIvMom5r9cFMqwZ+9QjngkaOjLfv8MIxK32FpRPXVmgOVuCp/OYgC4GqNg3LNdCnuCG68Y72QUsUpBQGlio+10S/Fw9lVO4irPqqBd/VmS3W6+jnlWbbrTyWipi/ZVJJsOjE+nUrQnkmTimtVpBJRNfdUsvraSCWj97elk7SlEmTTSdrS0bK1rzszSdoz0XRCAbuILGEK8EVEROZRNj37YD2dUIBvZlcAHwSSwD+5+3une38QOkeHC1SC8QC1EpfcV9ukh6ETeM3QbROex6vl1/YGP/l9ZkbCIGFGIhFNJ83i1wBGEI6P1x6VJMf5qK47dEKYUE0/agoQVb2vTYMozT2qXVCdBuIq+uOB+MTnMMp/dbrakV5NW/z5lE5a1H48GbUpT6cSZOM25e2ZJKuSadLJKMBOJ410MhE/qmkJ2tNR4N0eP9riRzSdGAvEU7oBJiLSMAX4IiIi8yibmn1P+iu9ir6ZJYG/B14A7Ad+aGa3uPv9Uy3z2PFRrrnxrqbmq9rUfrax8cRS5MTYDYLqzQKrvibqmNEsfoY4bfw1FqcRry8OrNvj0upkzWclLSq1TiTGS7An9x2QTBjpRGJsmdrpVNzRW7UDuHRyYmdwmWSCdCoK1lXKLSKyOCnAFxERmUdz6Ul/pQf4wKXALnd/FMDMbgKuBKYM8Dd0ZXnb884cC0rHSoprAtbJneNVS92TY6Xx4+9LJJj4vjjIhokd44VxybgTPYfuJwTzapMtIiKtogBfRERknrWlZ9eTvqrosw3YV/N6P/CUyW8ys2uBawG2bNvBC87bvCCZMzOSBkkUuIuIyOKmAF9ERGSeZVONB+yJBCSmGHZtBam3A06oGO/uNwA3ADzhSRf7pp7sWPvy6nB01fbzoXtNNfiaKvDV0vn4Iz3+mKi9e9wWfuzzxqvTV6vZA2Nt8avrdya2lw9r29PXpNeuP5o/nl67A8YrANiE12Hcnr5ac0C99ouIyGQK8EVEROZZNt14T/oZdSAGUYn9jprX24GD0y2QShgbe9qamqnFLqzpQC+saUbgEzryi24oTL7pABM78Bu/qTHxZkltJ4Qnc0OhXqsF3aAQEZl/CvBFRETmWdssetJXD+EA/BA4y8xOAw4AVwG/0tosLX6JhJHAmMX9pJMyVoPAfawGhNl47YOxjgEZD+hn6o+g9gbC2AgG8Q2GCTclmHQnIp6s1rAY71eBmn4UotflwOPh/ILx4fyCkHJFdxhEZPlRgC8iIjLPsqlkwz3pp1Q9H3evmNlvAV8lGibvn939vhZnSyap3lCYT9We/ZsplYT2TBJIT0gPQ48C/SA8oYnHxOETx4diDBvvWkNEpCUU4IuIiDRBWzpBvjRzNJBWCT4A7v4V4CutzoesHImE0ZZI0jaLKhBBHOhHD6cSxLUBAqdQDqgEqhUgIq2lAF9ERKQJsqlkQwG+hsgTWTqiGgdT3xQoByH5ckChHFAoRdOlior9RWThKMAXERFpgmyD7fA1RJ7I8pFOJkgnE/S0jTcHCEInXw4oloMTOi8ce7hTCTQygoicPAX4IiIiTdBotd90SiX4IstZMmF0ZVN0ZWe+7B6rAVAKyJcDciVV+xeR2VGALyIi0gTZVGMl8ymV4ItIrF4NgMlBf74caAQAEZmSAnwREZEmaLQn/bTa4IvINOoF/ZUgJKegX0TqUIAvIiLSJDP1pJ9M2IzjhIuITJZKJuiZKugvBxTLIcVKSKkSEoQK/EVWEgX4IiIiTTJTT/oqvReR+VIv6Ico8K8G+8VKSLGitv0iy5kCfBERkSaZqSf9VFLt70WkuVLJBKlkgs7sxPRCOWC4UGGkWGG0WFEP/iLLhAJ8ERGRJpmpJ32V4ItIq7Slk7Slk2zozhKGzmgpCvZHChUK5alrHonI4qYAX0REpEnaUjMF+CrBF5HWSySM7rY03W1pWBX13D+QKzOYL03bzEhEFp+WXlmY2e+ZmZvZ+pq068xsl5k9ZGYvqkm/2Mzujef9ncW9EplZ1sw+G6f/wMx2tmBTRERETpBJJZiuD71UQiX4IrL4pJMJNnRnOXNjN2dt6mJDd5Z0Sr9XIktBywJ8M9sBvADYW5N2HnAV8HjgCuAjZlYt/vgocC1wVvy4Ik6/Buh39zOBvwXetyAbICIi0oDpqumrDb6ILHZt6SSbV7Vx7uYeTtvQyeqONAn9dIksWq2sov+3wB8AX6pJuxK4yd2LwGNmtgu41Mx2Az3u/j0AM/sE8Arg1niZ98TLfx74sJmZu7oKERGR1sumEuRLQd15aoMvIktJVzZFVzZFGDojpQqFUkChHFKoBJQq4Ywd9ZlFNZvaUkna0gmy6STJhJEwMAyz6D0JM4zo2YF8OSBXqlAoheTKFcoVXeavFGbQnon6i+gbKbU6O0tCSwJ8M3s5cMDdfzpp/N9twPdrXu+P08rx9OT06jL7ANy9YmaDwDqgt87nXktUC4BTTjllXrZFRERkOlEJfrnuPLXBF5GlKJEwetrSE4bkC0OnWAkplAPy5YBCObqx2ZZO0h536JdNJUjMoWlS9cZCVTkIyZcD8qVoyL9iJZhz0G8W/RankkYlcMrBzDcqpLlSSaMrm6KnLU1XW4pkfMx0ZVPs68vp/zODpgX4ZvYNYHOdWX8E/CHwwnqL1UnzadKnW+bERPcbgBsALrnkEh0aIiLSdG3TDJWnNvgislwkEkZ7Jkl7JsmaJn9WOpkgnUxMuMHgHt1gKAUhpUr0KFeng5BkwsimkmRSCbKpBJlUgkwymp5U4Eg5iJYtV5xSEFIJo+nRUoVKsHJCiOpuaXZAbRbdCOppS9HdlqY9U79p26r2NJkNXew+Prqi/g+z1bQA392fXy/dzC4ATgOqpffbgR+Z2aVEJfM7at6+HTgYp2+vk07NMvvNLAWsAvrmb0tERETmLjtFT/qppJ1wUSkiInNjZmND/52s6g0EMhPT3Z3hYoX+0RLDhcqyLklOJY1T13XQkYmaZATuBKETVp9DqIQhoUf7xYHQHfeopNWr0x4F8MmEkUpY/BzVmKimzaY/mvZMkjM2dLHn+KiGc5zCglfRd/d7gY3V13H7+kvcvdfMbgE+bWbvB7YSdaZ3p7sHZjZsZpcBPwDeCHwoXsUtwNXA94BXAbep/b2IiCwWmVSCRALCSdchan8vIrK0mI03TSgHIf25Ev2jZUqV5RVotmcSnLquc6wZWSJhJDDm4d7JvMikEpy+oYu9fTlGCpVWZ2fRaWUneydw9/vM7GbgfqACvNXdqz0TvQX4ONBO1LnerXH6x4BPxh3y9RH1wi8iIrJoZFPJEzraS6kbahGRJSudTLCxu42N3W2MFiv0jZYYzJeXfKn+6o4021a3z6mvhIWUTBg713VwYCBP/2j9fm5WqpYH+O6+c9Lr64Hr67zvLuD8OukF4NXNyp+IiMjJakuf2JN+SiX4IiLLQmc2RWc2xdbQxzoYzMed/xXKS6fTvk2rsmzsbmt1NhpmZmxf00E2VeTwYKHV2Vk0Wh7gi4iILHf1etLPqAd9EZFlJZmwE3r8r3b+VyhHwX6+HDBaXFzt9xMJ2LG2Y0KnhUvJhu4smWSCff3qYR8U4IuIiDRdNnViMD+bToVERGRpqtf5n7uTK0WB/kixQq4UzCowNZu/nu0zqQSnruuYl84JW2lVR5psuov9/fkTasytNArwRUREmqzehZOq6IuIrExmNlatfyMQhk6uPB7wl4OQdDIaxi/q0T/qaT5TMx2G0RB+xXJIMQgolsOx10HYWPTfmU1yytqOZXPDuS2d5MyNXRwbLnJkqLBiS/MV4IuIiDRZOnliT/ppdbInIiJEvdRXq/ZvmsUybYlqzYCJVeuD0Md69q+OxmoGhsXP0U2G5CLvSG+uNnRn6W5LcWAgT6648krzFeCLiIgsgLZ0csKFhkrwRUSkGZIJoz2ztKvcn6y2dJIzNqzM0nwVH4iIiCyA2nb4ZoyNLywiIiLNsaE7y1mbuujIznzDI5FYHjffVYIvIiKyAGp70l+u1SJFREQWm2wqKs3vHSmSLwWkkwlSSSOdiJ6r04n43JwrVegbLTGQKy/Jkn8F+CIiIgugtqM9ld6LiIgsrPVd2Ybe15FJ0ZFJsXWVM5Av0zdaJF8KZ15wkVCALyIisgBqq+inl0EVQBERkeUskTDWdmZY25khXwroy5UYyJUmdJi7GCnAFxERWQDpZIJkwghCXzZDEomIiKwE7Zkk2zLtbOlpY6hQZjBfZrhQWZRV+BXgi4iILJBsOkGuGJBWG3wREZElJ5EwVndkWN2RIQid4UUY7CvAFxERWSDVofJUgi8iIrK0JRdpsK8AX0REZIG0xe3w1QZfRERk+Zgc7B8azNM/Wm5JXlSEICIiskCycU/66kVfRERkeUomjO1rOti+ph1rwf18XWGIiIgskGoJfkpt8EVERJa1NZ0ZztzYRTa9sCG3AnwREZEFkkomSKdMbfBFRERWgLZ0kjM2dLGqPb1gn6krDBERkQXUmVH3NyIiIitFMmGcsq6DLavbFqTKvgJ8ERGRBdTdpgBfRERkpVnfleX0DZ2kU82N8nWVISIisoC6sjr1ioiIrEQdmRRnbuhitBg07TPMWzlIXwuZ2TFgT6vz0aD1QG+rM7FMad82j/Zt82jfNs9S2renuvuGVmeiVXQel5j2bfNo3zaH9mvzLLV925Tz+IoN8JcSM7vL3S9pdT6WI+3b5tG+bR7t2+bRvpVm0HHVPNq3zaN92xzar82jfRtRG3wRERERERGRZUABvoiIiIiIiMgyoAB/abih1RlYxrRvm0f7tnm0b5tH+1aaQcdV82jfNo/2bXNovzaP9i1qgy8iIiIiIiKyLKgEX0RERERERGQZUIAvIiIiIiIisgwowG8RM/tnMztqZj+rSbvIzL5nZvea2ZfNrKdm3oXxvPvi+W1x+sXx611m9ndmZq3YnsVkNvvWzF5nZj+peYRm9oR4nvZtjVnu17SZ3RinP2Bm19Uso/06ySz3bcbM/iVO/6mZPadmGe3bScxsh5n9d3wc3mdmb4/T15rZ183s5/Hzmpplrov34UNm9qKadO1fAXQObyadw5tH5/Hm0Xm8OXQOnyN316MFD+BZwJOAn9Wk/RB4djz9ZuDP4ukUcA9wUfx6HZCMp+8EngoYcCvw4lZvW6sfs9m3k5a7AHi05rX27Rz3K/ArwE3xdAewG9ip/Tov+/atwL/E0xuBu4GE9u2U+3YL8KR4uht4GDgP+Cvg3XH6u4H3xdPnAT8FssBpwCP6vdVj8kPn8MWxbyctp3P4PO5bncebum91Hm98v+ocPoeHSvBbxN2/DfRNSj4H+HY8/XXgl+LpFwL3uPtP42WPu3tgZluAHnf/nkdH7ieAVzQ984vcLPdtrV8GPgOgfXuiWe5XBzrNLAW0AyVgSPu1vlnu2/OAb8bLHQUGgEu0b+tz90Pu/qN4ehh4ANgGXAncGL/tRsb31ZVEF7VFd38M2AVcqv0rtXQObx6dw5tH5/Hm0Xm8OXQOnxsF+IvLz4CXx9OvBnbE02cDbmZfNbMfmdkfxOnbgP01y++P0+REU+3bWq8lvjhA+7ZRU+3XzwOjwCFgL/DX7t6H9utsTLVvfwpcaWYpMzsNuDiep307AzPbCTwR+AGwyd0PQXQBQVSKAtE+21ezWHU/av/KTHQObx6dw5tH5/Hm0Xl8Hukc3jgF+IvLm4G3mtndRNVQSnF6CngG8Lr4+RfN7HKiKiaTadzD+qbatwCY2VOAnLtX205p3zZmqv16KRAAW4mqSL3TzE5H+3U2ptq3/0x0YroL+ABwB1BB+3ZaZtYFfAH4HXcfmu6tddJ8mnSRKp3Dm0fn8ObRebx5dB6fJzqHz06q1RmQce7+IFFVPszsbOCl8az9wLfcvTee9xWidj6fArbXrGI7cHDBMryETLNvq65i/M4/RPtc+3YG0+zXXwH+y93LwFEz+y5wCfA/aL82ZKp96+4V4B3V95nZHcDPgX60b+syszTRhcG/uvu/xclHzGyLux+Kq+4djdP3M7F0sLof9Zsg09I5vHl0Dm8encebR+fx+aFz+OypBH8RMbON8XMC+GPgH+JZXwUuNLOOuC3Us4H74yopw2Z2WdwT5BuBL7Ug64veNPu2mvZq4KZqmvZtY6bZr3uB51mkE7gMeFD7tXFT7dv4d6Aznn4BUHF3/R5MId4XHwMecPf318y6Bbg6nr6a8X11C3CVmWXjqpNnAXdq/8pMdA5vHp3Dm0fn8ebRefzk6Rw+R63q3W+lP4juNB8CykR3la4B3k7UO+TDwHsBq3n/64H7iNrz/FVN+iVx2iPAh2uXWamPOezb5wDfr7Me7ds57legC/hcfMzeD/y+9uu87dudwENEHc18AzhV+3baffsMomp49wA/iR8vIerJ/JtEpSbfBNbWLPNH8T58iJpedrV/9ag5FnQOXzz7VufwJuxbncebum91Hm98v+ocPodH9UATERERERERkSVMVfRFRERERERElgEF+CIiIiIiIiLLgAJ8ERERERERkWVAAb6IiIiIiIjIMqAAX0RERERERGQZUIAvIiIiIiIisgwowBcRERERERFZBv5/eK3vgPMDQX0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_with_std(x, y, stds, ax, title, y_label):\n",
    "    ax.fill_between(x, y - stds, y + stds, alpha=0.2)\n",
    "    plot(x, y, ax, title, y_label)\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2)\n",
    "title = 'Increase in mean and std Fortune 500 company %s from 1955 to 2005'\n",
    "stds1 = group_by_year.std().profit.values\n",
    "stds2 = group_by_year.std().revenue.values\n",
    "plot_with_std(x, y1.values, stds1, ax1, title % 'profits', 'Profit (millions)')\n",
    "plot_with_std(x, y2.values, stds2, ax2, title % 'revenues', 'Revenue (millions)')\n",
    "fig.set_size_inches(14, 4)\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4401ff5c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
